Thomas S. Adams, Duncan Meacher, James Clark, Patrick J. Sutton, Gareth Jones, Ariana Minot
Searches for gravitational-wave bursts (short duration and narrowband clusters of excess power in the signal streams) require identification of weak signals from background detector noise. The sensitivity of such searches is often critically limited by non-Gaussian noise fluctuations which are difficult to distinguish from real signals, posing a key problem for transient gravitational-wave astronomy. Current noise rejection tests are based on the analysis of a relatively small number of measured properties of the candidate signal, typically correlations between detectors. Multivariate analysis (MVA) techniques probe the full space of measured properties of events (clusters of excess power in the signal streams) in an attempt to maximise the power to accurately classify events as signal or background. This is done by taking samples of known background events and (simulated) signal events to train the MVA classifier, which can then be applied to classify events of unknown type. We apply the boosted decision tree (BDT) MVA technique to the problem of detecting gravitational-wave bursts associated with gamma-ray bursts. We find that BDTs are able to increase the sensitive distance reach of the search by as much as 50% corresponding to a factor of ~3 increase in sensitive volume. This improvement is robust against trigger sky position, large sky localisation error, poor data quality, and the simulated signal events waveforms that are used. Critically, we find that the BDT analysis is able to detect signals that have different morphologies to those used in the classifier training and that this improvement extends down to a false alarm probability below the 3{\sigma} significance level. These indicate that MVA techniques may be used for the robust detection of gravitational-wave bursts with a priori unknown waveform.
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http://arxiv.org/abs/1305.5714
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